Metagenomic next-generation sequencing of bronchoalveolar lavage fluid assists in the diagnosis of pathogens associated with lower respiratory tract infections in children

Worldwide, lower respiratory tract infections (LRTI) are an important cause of hospitalization in children. Due to the relative limitations of traditional pathogen detection methods, new detection methods are needed. The purpose of this study was to evaluate the value of metagenomic next-generation sequencing (mNGS) of bronchoalveolar lavage fluid (BALF) samples for diagnosing children with LRTI based on the interpretation of sequencing results. A total of 211 children with LRTI admitted to the First Affiliated Hospital of Guangzhou Medical University from May 2019 to December 2020 were enrolled. The diagnostic performance of mNGS versus traditional methods for detecting pathogens was compared. The positive rate for the BALF mNGS analysis reached 95.48% (95% confidence interval [CI] 92.39% to 98.57%), which was superior to the culture method (44.07%, 95% CI 36.68% to 51.45%). For the detection of specific pathogens, mNGS showed similar diagnostic performance to PCR and antigen detection, except for Streptococcus pneumoniae, for which mNGS performed better than antigen detection. S. pneumoniae, cytomegalovirus and Candida albicans were the most common bacterial, viral and fungal pathogens. Common infections in children with LRTI were bacterial, viral and mixed bacterial-viral infections. Immunocompromised children with LRTI were highly susceptible to mixed and fungal infections. The initial diagnosis was modified based on mNGS in 29.6% (37/125) of patients. Receiver operating characteristic (ROC) curve analysis was performed to predict the relationship between inflammation indicators and the type of pathogen infection. BALF mNGS improves the sensitivity of pathogen detection and provides guidance in clinical practice for diagnosing LRTI in children

Monoclonal Antibodies against Accumulation-Associated Protein Affect EPS Biosynthesis and Enhance Bacterial Accumulation of Staphylococcus epidermidis

Because there is no effective antibiotic to eradicate Staphylococcus epidermidis biofilm infections that lead to the failure of medical device implantations, the development of anti-biofilm vaccines is necessary. Biofilm formation by S. epidermidis requires accumulation-associated protein (Aap) that contains sequence repeats known as G5 domains, which are responsible for the Zn2+-dependent dimerization of Aap to mediate intercellular adhesion. Antibodies against Aap have been reported to inhibit biofilm accumulation. In the present study, three monoclonal antibodies (MAbs) against the Aap C-terminal single B-repeat construct followed by the 79-aa half repeat (AapBrpt1.5) were generated. MAb18B6 inhibited biofilm formation by S. epidermidis RP62A to 60% of the maximum, while MAb25C11 and MAb20B9 enhanced biofilm accumulation. All three MAbs aggregated the planktonic bacteria to form visible cell clusters. Epitope mapping revealed that the epitope of MAb18B6, which recognizes an identical area within AapBrpt constructs from S. epidermidis RP62A, was not shared by MAb25C11 and MAb20B9. Furthermore, all three MAbs were found to affect both Aap expression and extracellular polymeric substance (EPS, including extracellular DNA and PIA) biosynthesis in S. epidermidis and enhance the cell accumulation. These findings contribute to a better understanding of staphylococcal biofilm formation and will help to develop epitope-peptide vaccines against staphylococcal infections

Evaluating the Above-Ground Carbon Storage of Urban Trees on University of British Columbia Vancouver Campus

In response to growing concerns about the impacts of climate change and the need for sustainable urban development, urban forests have emerged as a crucial tool for mitigating climate change impacts and enhancing the quality of life in cities. Previous studies have established that urban forests provide a wide range of ecosystem services, including air purification, temperature regulation, and carbon sequestration. However, precise estimation of urban tree carbon storage remains a key challenge for effective urban forest management and planning. In this work, we expand on this body of work by investigating the carbon storage of trees on the UBC Vancouver Campus using 2018 Light Detection and Ranging (LiDAR) data sourced from the City of Vancouver. The aim was to determine the total carbon storage and the average carbon density of the campus. Tree height and structure were estimated using an existing model, which facilitated the calculation of individual tree biomass and carbon storage based on the LiDAR data. The results revealed that the UBC Vancouver Campus has a total carbon storage of 24.63 Gg and an average carbon density of 6.13 kg m-2. These findings emphasize the significant role urban forests play in climate change mitigation and urban life improvement. Employing LiDAR data in conjunction with the existing model proved to be an efficient and effective method for estimating urban tree carbon storage. The results can inform urban planning and policy decisions, fostering the integration of urban forests into sustainable campus development. Disclaimer: “UBC SEEDS provides students with the opportunity to share the findings of their studies, as well as their opinions, conclusions and recommendations with the UBC community. The reader should bear in mind that this is a student project/report and is not an official document of UBC. Furthermore readers should bear in mind that these reports may not reflect the current status of activities at UBC. We urge you to contact the research persons mentioned in a report or the SEEDS Coordinator about the current status of the subject matter of a project/report.”Forestry, Faculty ofForest and Conservation Sciences, Department ofUnreviewedGraduat

Improved CNN-Based Hashing for Encrypted Image Retrieval

As more and more image data are stored in the encrypted form in the cloud computing environment, it has become an urgent problem that how to efficiently retrieve images on the encryption domain. Recently, Convolutional Neural Network (CNN) features have achieved promising performance in the field of image retrieval, but the high dimension of CNN features will cause low retrieval efficiency. Also, it is not suitable to directly apply them for image retrieval on the encryption domain. To solve the above issues, this paper proposes an improved CNN-based hashing method for encrypted image retrieval. First, the image size is increased and inputted into the CNN to improve the representation ability. Then, a lightweight module is introduced to replace a part of modules in the CNN to reduce the parameters and computational cost. Finally, a hash layer is added to generate a compact binary hash code. In the retrieval process, the hash code is used for encrypted image retrieval, which greatly improves the retrieval efficiency. The experimental results show that the scheme allows an effective and efficient retrieval of encrypted images

Nrf2-Knockout Protects from Intestinal Injuries in C57BL/6J Mice Following Abdominal Irradiation with γ Rays

Radiation-induced intestinal injuries (RIII) commonly occur in patients who suffer from pelvic or abdominal cancer. Nuclear factor-erythroid 2-related factor 2 (Nrf2) is a key transcriptional regulator of antioxidant, and the radioprotective role of Nrf2 is found in bone marrow, lung, and intestine, etc. Here, we investigated the effect of Nrf2 knockout on radiation-induced intestinal injuries using Nrf2 knockout (Nrf2−/−) mice and wild-type (Nrf2+/+) C57BL/6J mice following 13 Gy abdominal irradiation (ABI). It was found that Nrf2 knockout promoted the survival of irradiated mice, protected the crypt-villus structure of the small intestine, and elevated peripheral blood lymphocyte count and thymus coefficients. The DNA damage of peripheral blood lymphocytes and the apoptosis of intestinal epithelial cells (IECs) of irradiated Nrf2−/− mice were decreased. Furthermore, compared with that of Nrf2+/+ mice, Nrf2 knockout increased the number of Lgr5+ intestinal stem cells (ISCs) and their daughter cells including Ki67+ transient amplifying cells, Villin+ enterocytes, and lysozyme+ Paneth cells. Nuclear factor-κB (NF-κB) was accumulated in the crypt base nuclei of the small intestine, and the mRNA expression of NF-κB target genes Bcl-2, uPA, and Xiap of the small intestine from irradiated Nrf2−/− mice were increased. Collectively, Nrf2 knockout has the protective effect on small intestine damage following abdominal irradiation by prompting the proliferation and differentiation of Lgr5+ intestinal stem cells and activation of NF-κB

Fast Detection of the Single Point Leakage in Branched Shale Gas Gathering and Transportation Pipeline Network with Condensate Water

The node pressure and flow rate along the shale gas flow process are analyzed according to the characteristics of the shale gas flow pipe network, and the non-leaking and leaking processes of the shale gas flow pipe network are modeled separately. The changes in pressure over time along each pipe segment in the network provide new ideas for identifying leaking pipe sections. This paper uses the logarithmic value of pressure as the basis for judging whether the flow pipe network is leaking or not, according to the process of varying flow parameters resulting in the regularity of leakage. A graph of the change in pressure of the pipe section after the leak compared to the pressure of the non-leaking section of pipe over time can be plotted, accurately identifying the specific section of pipe with the leak. The accuracy of this novel method is verified by the leakage section and statistical data of the shale gas pipeline network in situ used in this paper

Unsupervised Graph Structure Learning Based on Optimal Graph Topology Modeling and Adaptive Data Augmentation

Graph neural networks (GNNs) are effective for structured data analysis but face reduced learning accuracy due to noisy connections and the necessity for explicit graph structures and labels. This requirement constrains their usability in diverse graph-based applications. In order to address these issues, considerable research has been directed toward graph structure learning that aims to denoise graph structures concurrently and refine GNN parameters. However, existing graph structure learning approaches encounter several challenges, including dependence on label information, underperformance of learning algorithms, insufficient data augmentation methods, and limitations in performing downstream tasks. We propose Uogtag, an unsupervised graph structure learning framework to address these challenges. Uogtag optimizes graph topology through the selection of suitable graph learners for the input data and incorporates contrastive learning with adaptive data augmentation, enhancing the learning and applicability of graph structures for downstream tasks. Comprehensive experiments on various real-world datasets demonstrate Uogtag’s efficacy in managing noisy graphs and label scarcity